Landslides significantly threaten lives and infrastructure, especially in seismically active regions. This study conducts a probabilistic analysis of seismic landslide runout behavior, leveraging a large-deformation f...Landslides significantly threaten lives and infrastructure, especially in seismically active regions. This study conducts a probabilistic analysis of seismic landslide runout behavior, leveraging a large-deformation finite-element (LDFE) model that accounts for the three-dimensional (3D) spatial variability and cross-correlation in soil strength — a reflection of natural soils' inherent properties. LDFE model results are validated by comparing them against previous studies, followed by an examination of the effects of univariable, uncorrelated bivariable, and cross-correlated bivariable random fields on landslide runout behavior. The study's findings reveal that integrating variability in both friction angle and cohesion within uncorrelated bivariable random fields markedly influences runout distances when compared with univariable random fields. Moreover, the cross-correlation of soil cohesion and friction angle dramatically affects runout behavior, with positive correlations enlarging and negative correlations reducing runout distances. Transitioning from two-dimensional (2D) to 3D analyses, a more realistic representation of sliding surface, landslide velocity, runout distance and final deposit morphology is achieved. The study highlights that 2D random analyses substantially underestimate the mean value and overestimate the variability of runout distance, underscoring the importance of 3D modeling in accurately predicting landslide behavior. Overall, this work emphasizes the essential role of understanding 3D cross-correlation in soil strength for landslide hazard assessment and mitigation strategies.展开更多
Since the damages caused by disasters associated with climate anomalies and the diversification of the social structure increase every year, an efficient management system associated with a damage assessment of the ar...Since the damages caused by disasters associated with climate anomalies and the diversification of the social structure increase every year, an efficient management system associated with a damage assessment of the areas vulnerable to disasters is demanded to prevent or mitigate the damages to infrastructure. The areas vulnerable to disasters in Busan, located at southeastern part of Korea, were estimated based on historical records of damages and a risk assessment of the infrastructure was performed to provide fundamental information prior to the establishment of the real-time monitoring system for infrastructure and establish disaster management system. The results are illustrated by using geographical information system(GIS) and provide the importance of the roadmap for comprehensive and specific strategy to manage natural disasters.展开更多
The e-commerce industry has experienced significant growth in the past decade,particu-larly post-COVID.To accommodate such growth,the parcel delivery sector has also grown rapidly.However,there is a lack of study that...The e-commerce industry has experienced significant growth in the past decade,particu-larly post-COVID.To accommodate such growth,the parcel delivery sector has also grown rapidly.However,there is a lack of study that properly evaluates its social and environ-mental impacts at a large scale.A model is proposed to analyze such impacts.A parcel gen-eration process is presented to convert public data into parcel volumes and stops.A continuous approximation model is fitted to estimate the length of parcel service tours.A case study is conducted using New York City(NYC)data.The parcel generation is shown to be a valid fit.The continuous approximation model parameters have R2 values of 98%or higher.The model output is validated against UPS truck trips.Application of the model to 2021 suggests residential parcel deliveries contributed to 0.05%of total daily vehicle-kilometer-traveled(VKT)in NYC corresponding to 14.4 metric tons of carbon equivalent(MTCE)emissions per day.COVID-19 contributed to an increase in parcel deliveries that led to up to 1064.3 MTCE of annual greenhouse gas(GHG)emissions in NYC(which could power 532 standard US households for a year).The existing bike lane infrastructure can support the substitution of 17%of parcel deliveries by cargo bikes,which would reduce VKT by 11%.Adding 3 km of bike lanes to connect Amazon facilities can expand their cargo bike substitution benefit from a VKT reduction of 5%up to 30%.If 28 km of additional bike lanes are made,parcel delivery substitution citywide could increase from 17%to 34%via cargo bike and save an additional 2.3 MTCE per day.Cargo bike priorities can be set to reduce GHG emissions for lower-income neighborhoods including Harlem,Sunset Park,and Bushwick.展开更多
Nowadays,electric vehicles(EVs)are increasingly equipped with advanced onboard devices capable of collecting and recording real-time charging data.The analysis of such data from a large-scale EV fleet plays a crucial ...Nowadays,electric vehicles(EVs)are increasingly equipped with advanced onboard devices capable of collecting and recording real-time charging data.The analysis of such data from a large-scale EV fleet plays a crucial role in supporting decision-making processes,particularly in the deployment of charging infrastructure and the formulation of EV-focused policies.Nevertheless,the challenges of collecting these data are significant,primarily due to privacy concerns and the high costs associated with data access.In response,this study introduces an innovative methodology for generating large-scale and diverse EV charging data,mirroring real-world patterns for cost-efficient and privacy-compliant use.Specifically,this approach combines Gibbs sampling and conditional density networks and was trained and validated using a real-world dataset consisting of approximately 1.65 million charging events from 3,777 battery EVs(BEVs)in Shanghai over a year.Results illustrate that the proposed model can effectively capture the underlying distribution of the original charging data,enabling the generation of synthetic samples that closely resemble real-world charging events.The approach is readily employed for data imputation and augmentation,and it can also help simulate future charging distributions by conditional generation based on anticipated development premises.展开更多
基金supported by the National Natural Science Foundation of China(Grant No.U22A20596)the Shenzhen Science and Technology Program(Grant No.GJHZ20220913142605010)the Jinan Lead Researcher Project(Grant No.202333051).
文摘Landslides significantly threaten lives and infrastructure, especially in seismically active regions. This study conducts a probabilistic analysis of seismic landslide runout behavior, leveraging a large-deformation finite-element (LDFE) model that accounts for the three-dimensional (3D) spatial variability and cross-correlation in soil strength — a reflection of natural soils' inherent properties. LDFE model results are validated by comparing them against previous studies, followed by an examination of the effects of univariable, uncorrelated bivariable, and cross-correlated bivariable random fields on landslide runout behavior. The study's findings reveal that integrating variability in both friction angle and cohesion within uncorrelated bivariable random fields markedly influences runout distances when compared with univariable random fields. Moreover, the cross-correlation of soil cohesion and friction angle dramatically affects runout behavior, with positive correlations enlarging and negative correlations reducing runout distances. Transitioning from two-dimensional (2D) to 3D analyses, a more realistic representation of sliding surface, landslide velocity, runout distance and final deposit morphology is achieved. The study highlights that 2D random analyses substantially underestimate the mean value and overestimate the variability of runout distance, underscoring the importance of 3D modeling in accurately predicting landslide behavior. Overall, this work emphasizes the essential role of understanding 3D cross-correlation in soil strength for landslide hazard assessment and mitigation strategies.
基金Project supported by the 2013 Inje University Research Grant of Korea
文摘Since the damages caused by disasters associated with climate anomalies and the diversification of the social structure increase every year, an efficient management system associated with a damage assessment of the areas vulnerable to disasters is demanded to prevent or mitigate the damages to infrastructure. The areas vulnerable to disasters in Busan, located at southeastern part of Korea, were estimated based on historical records of damages and a risk assessment of the infrastructure was performed to provide fundamental information prior to the establishment of the real-time monitoring system for infrastructure and establish disaster management system. The results are illustrated by using geographical information system(GIS) and provide the importance of the roadmap for comprehensive and specific strategy to manage natural disasters.
基金support from C2SMART University Transportation Center(USDOT#69A3551747124).
文摘The e-commerce industry has experienced significant growth in the past decade,particu-larly post-COVID.To accommodate such growth,the parcel delivery sector has also grown rapidly.However,there is a lack of study that properly evaluates its social and environ-mental impacts at a large scale.A model is proposed to analyze such impacts.A parcel gen-eration process is presented to convert public data into parcel volumes and stops.A continuous approximation model is fitted to estimate the length of parcel service tours.A case study is conducted using New York City(NYC)data.The parcel generation is shown to be a valid fit.The continuous approximation model parameters have R2 values of 98%or higher.The model output is validated against UPS truck trips.Application of the model to 2021 suggests residential parcel deliveries contributed to 0.05%of total daily vehicle-kilometer-traveled(VKT)in NYC corresponding to 14.4 metric tons of carbon equivalent(MTCE)emissions per day.COVID-19 contributed to an increase in parcel deliveries that led to up to 1064.3 MTCE of annual greenhouse gas(GHG)emissions in NYC(which could power 532 standard US households for a year).The existing bike lane infrastructure can support the substitution of 17%of parcel deliveries by cargo bikes,which would reduce VKT by 11%.Adding 3 km of bike lanes to connect Amazon facilities can expand their cargo bike substitution benefit from a VKT reduction of 5%up to 30%.If 28 km of additional bike lanes are made,parcel delivery substitution citywide could increase from 17%to 34%via cargo bike and save an additional 2.3 MTCE per day.Cargo bike priorities can be set to reduce GHG emissions for lower-income neighborhoods including Harlem,Sunset Park,and Bushwick.
基金National Natural Science Foundation of China(72101153 and 72061127003)Shanghai Chenguang Program(21CGA72),Shanghai Eastern Scholar Program(QD2020057)+1 种基金Shanghai Frontiers Science Center of Artificial Intelligence and Deep Learning at NYU ShanghaiNYU Shanghai Doctoral Fellowships,and the NYU Shanghai Boost Fund.
文摘Nowadays,electric vehicles(EVs)are increasingly equipped with advanced onboard devices capable of collecting and recording real-time charging data.The analysis of such data from a large-scale EV fleet plays a crucial role in supporting decision-making processes,particularly in the deployment of charging infrastructure and the formulation of EV-focused policies.Nevertheless,the challenges of collecting these data are significant,primarily due to privacy concerns and the high costs associated with data access.In response,this study introduces an innovative methodology for generating large-scale and diverse EV charging data,mirroring real-world patterns for cost-efficient and privacy-compliant use.Specifically,this approach combines Gibbs sampling and conditional density networks and was trained and validated using a real-world dataset consisting of approximately 1.65 million charging events from 3,777 battery EVs(BEVs)in Shanghai over a year.Results illustrate that the proposed model can effectively capture the underlying distribution of the original charging data,enabling the generation of synthetic samples that closely resemble real-world charging events.The approach is readily employed for data imputation and augmentation,and it can also help simulate future charging distributions by conditional generation based on anticipated development premises.
